The autoregressive moving average (ARMA) model is a classical, and arguably one of the most studied approaches to model time series data. It has compelling theoretical properties and is widely used among practitioners. More recent deep learning approaches popularize recurrent neural networks (RNNs) and, in particular, Long Short-Term Memory (LSTM) cells that have become one of the best performing and most common building blocks in neural time series modeling. While advantageous for time series data or sequences with long-term effects, complex RNN cells are not always a must and can sometimes even be inferior to simpler recurrent approaches. In this work, we introduce the ARMA cell, a simpler, modular, and effective approach for time series modeling in neural networks. This cell can be used in any neural network architecture where recurrent structures are present and naturally handles multivariate time series using vector autoregression. We also introduce the ConvARMA cell as a natural successor for spatially-correlated time series. Our experiments show that the proposed methodology is competitive with popular alternatives in terms of performance while being more robust and compelling due to its simplicity

翻译：自回归移动平均（ARMA）模型是一种经典且可以说是研究最广泛的时间序列数据建模方法之一。它具有引人注目的理论性质，并在实践者中被广泛应用。更近期的深度学习方法推广了循环神经网络（RNN），特别是长短期记忆（LSTM）细胞，已成为神经时间序列建模中性能最优且最常见的构建模块之一。尽管对于具有长期效应的时间序列数据或序列具有优势，但复杂的RNN细胞并非总是必要，有时甚至可能不如简单的循环方法。在这项工作中，我们引入了ARMA细胞——一种更简单、模块化且有效的神经网络时间序列建模方法。该细胞可用于任何存在循环结构的神经网络架构，并通过向量自回归自然处理多变量时间序列。我们还引入了ConvARMA细胞作为空间相关时间序列的自然后续方法。实验表明，所提出的方法在性能上与流行的替代方案相当，同时由于其简单性而更具鲁棒性和说服力。